"""
Cross-Temporal Dialogue Generation System.

This module implements the core dialogue generation system for authentic
historical conversations with personality conditioning and real-time validation.
"""

import numpy as np
import logging
from typing import Dict, List, Optional, Tuple, Union, Any
from dataclasses import dataclass, field
from enum import Enum
import time
import asyncio
from concurrent.futures import ThreadPoolExecutor

from ..personality.models import PersonalityVector
from ..memory.manager import MemoryManager
from .llm_interface import LLMInterface
from .anachronism_detector import AnachronismDetector
from .cultural_adapter import CulturalAdapter

logger = logging.getLogger(__name__)


class DialogueMode(Enum):
    """Dialogue generation modes."""
    HISTORICAL_ACCURACY = "historical_accuracy"
    EDUCATIONAL = "educational"
    ENTERTAINMENT = "entertainment"
    RESEARCH = "research"


@dataclass
class DialogueContext:
    """Context for dialogue generation."""
    historical_period: str
    participants: List[str]
    topic: str
    location: str
    cultural_context: str
    time_period_start: int
    time_period_end: int
    language_style: str = "period_appropriate"
    formality_level: float = 0.7
    educational_objectives: List[str] = field(default_factory=list)


@dataclass
class GenerationRequest:
    """Request for dialogue generation."""
    speaker_id: str
    context: DialogueContext
    input_message: str
    conversation_history: List[Dict[str, str]] = field(default_factory=list)
    constraints: Dict[str, Any] = field(default_factory=dict)
    target_accuracy: float = 0.95
    max_response_length: int = 500
    temperature: float = 0.7


@dataclass
class GenerationResponse:
    """Response from dialogue generation."""
    generated_text: str
    accuracy_score: float
    cultural_authenticity: float
    anachronism_detected: bool
    confidence_score: float
    generation_time_ms: float
    metadata: Dict[str, Any] = field(default_factory=dict)


class CrossTemporalDialogueGenerator:
    """
    Advanced dialogue generation system for cross-temporal conversations.
    
    Features:
    - LLM integration with personality conditioning
    - Real-time historical accuracy validation (>95% target)
    - Cultural context adaptation
    - Anachronism prevention and correction
    - Multi-participant conversation management
    """
    
    def __init__(
        self,
        memory_manager: MemoryManager,
        llm_interface: LLMInterface,
        anachronism_detector: AnachronismDetector,
        cultural_adapter: CulturalAdapter,
        target_accuracy: float = 0.95,
        max_workers: int = 4
    ):
        """
        Initialize the dialogue generation system.
        
        Args:
            memory_manager: Memory system for personality storage
            llm_interface: Interface to language models
            anachronism_detector: System for detecting temporal inconsistencies
            cultural_adapter: System for cultural authenticity
            target_accuracy: Target historical accuracy (default 95%)
            max_workers: Maximum number of worker threads
        """
        self.memory_manager = memory_manager
        self.llm_interface = llm_interface
        self.anachronism_detector = anachronism_detector
        self.cultural_adapter = cultural_adapter
        self.target_accuracy = target_accuracy
        self.executor = ThreadPoolExecutor(max_workers=max_workers)
        
        # Performance tracking
        self.generation_stats = {
            'total_generations': 0,
            'successful_generations': 0,
            'accuracy_failures': 0,
            'anachronism_detections': 0,
            'average_generation_time': 0.0,
            'average_accuracy_score': 0.0
        }
        
        # Personality cache for faster access
        self.personality_cache = {}
        
        logger.info("CrossTemporalDialogueGenerator initialized")
    
    async def generate_response(
        self,
        request: GenerationRequest
    ) -> GenerationResponse:
        """
        Generate a historically accurate dialogue response.
        
        Args:
            request: Generation request with context and constraints
            
        Returns:
            Generated response with accuracy metrics
        """
        start_time = time.time()
        
        try:
            # Load speaker personality from memory
            personality = await self._load_personality(request.speaker_id)
            
            # Apply cultural adaptation
            adapted_context = await self._adapt_cultural_context(
                request.context, personality
            )
            
            # Generate initial response using LLM with personality conditioning
            initial_response = await self._generate_with_personality(
                request, personality, adapted_context
            )
            
            # Validate historical accuracy
            accuracy_score = await self._validate_historical_accuracy(
                initial_response, request.context
            )
            
            # Check for anachronisms
            anachronism_result = await self._check_anachronisms(
                initial_response, request.context
            )
            
            # Apply corrections if needed
            final_response = initial_response
            if accuracy_score < self.target_accuracy or anachronism_result.detected:
                final_response = await self._apply_corrections(
                    initial_response, request, anachronism_result
                )
                
                # Re-validate after corrections
                accuracy_score = await self._validate_historical_accuracy(
                    final_response, request.context
                )
                anachronism_result = await self._check_anachronisms(
                    final_response, request.context
                )
            
            # Calculate cultural authenticity
            cultural_score = await self._calculate_cultural_authenticity(
                final_response, request.context, personality
            )
            
            # Calculate confidence score
            confidence = self._calculate_confidence(
                accuracy_score, cultural_score, anachronism_result.confidence
            )
            
            generation_time = (time.time() - start_time) * 1000
            
            # Update statistics
            self._update_stats(accuracy_score, generation_time, anachronism_result.detected)
            
            return GenerationResponse(
                generated_text=final_response,
                accuracy_score=accuracy_score,
                cultural_authenticity=cultural_score,
                anachronism_detected=anachronism_result.detected,
                confidence_score=confidence,
                generation_time_ms=generation_time,
                metadata={
                    'speaker_id': request.speaker_id,
                    'historical_period': request.context.historical_period,
                    'personality_similarity': personality.get_vector_norm(),
                    'corrections_applied': accuracy_score < self.target_accuracy,
                    'validation_passes': 2 if accuracy_score < self.target_accuracy else 1
                }
            )
            
        except Exception as e:
            logger.error(f"Dialogue generation failed: {e}")
            self.generation_stats['total_generations'] += 1
            raise
    
    async def _load_personality(self, speaker_id: str) -> PersonalityVector:
        """Load personality vector for speaker from memory."""
        if speaker_id in self.personality_cache:
            return self.personality_cache[speaker_id]
        
        # Retrieve from memory system
        # This is a simplified implementation - in practice, would use
        # the memory manager to retrieve the personality vector
        personality = PersonalityVector(person_id=speaker_id)
        
        # Cache for future use
        self.personality_cache[speaker_id] = personality
        
        return personality
    
    async def _adapt_cultural_context(
        self, 
        context: DialogueContext, 
        personality: PersonalityVector
    ) -> DialogueContext:
        """Adapt dialogue context for cultural authenticity."""
        return await self.cultural_adapter.adapt_context(context, personality)
    
    async def _generate_with_personality(
        self,
        request: GenerationRequest,
        personality: PersonalityVector,
        adapted_context: DialogueContext
    ) -> str:
        """
        Generate response using LLM with personality conditioning.
        
        Implements: P(output|input) = P_base(output|input) × P_personality(output|P_HDC)
        """
        # Create personality-conditioned prompt
        personality_conditioning = self._create_personality_conditioning(
            personality, adapted_context
        )
        
        # Generate response using LLM interface
        response = await self.llm_interface.generate_response(
            prompt=request.input_message,
            context=adapted_context,
            personality_conditioning=personality_conditioning,
            conversation_history=request.conversation_history,
            temperature=request.temperature,
            max_length=request.max_response_length
        )
        
        return response
    
    def _create_personality_conditioning(
        self,
        personality: PersonalityVector,
        context: DialogueContext
    ) -> Dict[str, Any]:
        """Create personality conditioning parameters for LLM."""
        return {
            'big_five_traits': personality.big_five_traits.to_dict(),
            'cultural_dimensions': personality.cultural_dimensions.to_dict(),
            'historical_period': context.historical_period,
            'formality_level': context.formality_level,
            'language_style': context.language_style,
            'confidence_threshold': personality.confidence_score.overall_confidence
        }
    
    async def _validate_historical_accuracy(
        self,
        response: str,
        context: DialogueContext
    ) -> float:
        """Validate historical accuracy of generated response."""
        # Use multiple validation methods
        validations = await asyncio.gather(
            self._validate_factual_accuracy(response, context),
            self._validate_linguistic_accuracy(response, context),
            self._validate_social_context_accuracy(response, context)
        )
        
        # Weighted combination of validation scores
        weights = [0.4, 0.3, 0.3]
        accuracy_score = sum(w * v for w, v in zip(weights, validations))
        
        return min(accuracy_score, 1.0)
    
    async def _validate_factual_accuracy(
        self,
        response: str,
        context: DialogueContext
    ) -> float:
        """Validate factual accuracy against historical knowledge."""
        # This would interface with historical knowledge base
        # For now, return a placeholder score
        return 0.9
    
    async def _validate_linguistic_accuracy(
        self,
        response: str,
        context: DialogueContext
    ) -> float:
        """Validate linguistic appropriateness for time period."""
        # Check language patterns, vocabulary, grammar for period
        return 0.85
    
    async def _validate_social_context_accuracy(
        self,
        response: str,
        context: DialogueContext
    ) -> float:
        """Validate social and cultural context appropriateness."""
        return 0.92
    
    async def _check_anachronisms(
        self,
        response: str,
        context: DialogueContext
    ):
        """Check for temporal inconsistencies."""
        return await self.anachronism_detector.detect_anachronisms(
            response, context
        )
    
    async def _apply_corrections(
        self,
        response: str,
        request: GenerationRequest,
        anachronism_result
    ) -> str:
        """Apply corrections to improve accuracy and remove anachronisms."""
        corrections = []
        
        if anachronism_result.detected:
            corrections.extend(anachronism_result.corrections)
        
        # Apply cultural corrections
        cultural_corrections = await self.cultural_adapter.suggest_corrections(
            response, request.context
        )
        corrections.extend(cultural_corrections)
        
        # Generate corrected response
        corrected_response = await self._apply_correction_list(
            response, corrections, request
        )
        
        return corrected_response
    
    async def _apply_correction_list(
        self,
        response: str,
        corrections: List[Dict[str, str]],
        request: GenerationRequest
    ) -> str:
        """Apply a list of corrections to the response."""
        corrected = response
        
        for correction in corrections:
            if correction['type'] == 'replacement':
                corrected = corrected.replace(
                    correction['original'], 
                    correction['replacement']
                )
            elif correction['type'] == 'regeneration':
                # Regenerate specific parts with additional constraints
                corrected = await self._regenerate_with_constraints(
                    corrected, correction['constraints'], request
                )
        
        return corrected
    
    async def _regenerate_with_constraints(
        self,
        partial_response: str,
        constraints: Dict[str, Any],
        request: GenerationRequest
    ) -> str:
        """Regenerate parts of response with additional constraints."""
        # This would implement selective regeneration
        # For now, return the original response
        return partial_response
    
    async def _calculate_cultural_authenticity(
        self,
        response: str,
        context: DialogueContext,
        personality: PersonalityVector
    ) -> float:
        """Calculate cultural authenticity score."""
        return await self.cultural_adapter.calculate_authenticity(
            response, context, personality
        )
    
    def _calculate_confidence(
        self,
        accuracy_score: float,
        cultural_score: float,
        anachronism_confidence: float
    ) -> float:
        """Calculate overall confidence score."""
        weights = [0.4, 0.3, 0.3]
        scores = [accuracy_score, cultural_score, anachronism_confidence]
        
        confidence = sum(w * s for w, s in zip(weights, scores))
        return min(confidence, 1.0)
    
    def _update_stats(
        self,
        accuracy_score: float,
        generation_time: float,
        anachronism_detected: bool
    ):
        """Update generation statistics."""
        self.generation_stats['total_generations'] += 1
        
        if accuracy_score >= self.target_accuracy:
            self.generation_stats['successful_generations'] += 1
        else:
            self.generation_stats['accuracy_failures'] += 1
        
        if anachronism_detected:
            self.generation_stats['anachronism_detections'] += 1
        
        # Update running averages
        total = self.generation_stats['total_generations']
        self.generation_stats['average_generation_time'] = (
            (self.generation_stats['average_generation_time'] * (total - 1) + 
             generation_time) / total
        )
        
        self.generation_stats['average_accuracy_score'] = (
            (self.generation_stats['average_accuracy_score'] * (total - 1) + 
             accuracy_score) / total
        )
    
    def get_performance_metrics(self) -> Dict[str, Any]:
        """Get current performance metrics."""
        total = self.generation_stats['total_generations']
        
        if total == 0:
            return self.generation_stats.copy()
        
        metrics = self.generation_stats.copy()
        metrics.update({
            'success_rate': self.generation_stats['successful_generations'] / total,
            'accuracy_failure_rate': self.generation_stats['accuracy_failures'] / total,
            'anachronism_rate': self.generation_stats['anachronism_detections'] / total,
            'target_accuracy_met': (
                self.generation_stats['average_accuracy_score'] >= self.target_accuracy
            )
        })
        
        return metrics
    
    def reset_stats(self):
        """Reset performance statistics."""
        self.generation_stats = {
            'total_generations': 0,
            'successful_generations': 0,
            'accuracy_failures': 0,
            'anachronism_detections': 0,
            'average_generation_time': 0.0,
            'average_accuracy_score': 0.0
        }
        logger.info("Generation statistics reset")
    
    async def batch_generate(
        self,
        requests: List[GenerationRequest]
    ) -> List[GenerationResponse]:
        """Generate responses for multiple requests in parallel."""
        tasks = [self.generate_response(request) for request in requests]
        responses = await asyncio.gather(*tasks, return_exceptions=True)
        
        # Handle any exceptions
        results = []
        for i, response in enumerate(responses):
            if isinstance(response, Exception):
                logger.error(f"Batch generation failed for request {i}: {response}")
                # Create error response
                results.append(GenerationResponse(
                    generated_text="[Generation Error]",
                    accuracy_score=0.0,
                    cultural_authenticity=0.0,
                    anachronism_detected=True,
                    confidence_score=0.0,
                    generation_time_ms=0.0,
                    metadata={'error': str(response)}
                ))
            else:
                results.append(response)
        
        return results
    
    def shutdown(self):
        """Shutdown the dialogue generator."""
        self.executor.shutdown(wait=True)
        logger.info("CrossTemporalDialogueGenerator shutdown complete")